DE analysis - Day1
Sergey Naumenko
2021-03-26
1 Overview
- Principal Investigator: Beth Overmoyer
- Experiment: RNAseq_analysis_of_inflammatory_breast_cancer_hbc04141
- study 6 was excluded because if low read depth in 3373-3
- https://www.bioconductor.org/packages/release/bioc/vignettes/DEGreport/inst/doc/DEGreport.html
- AnnotationHub. We use ensembl version matching bcbio pipeline - v94.
- HBC materials
- HBC materials - functional analysis
- http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html
- this is DE for Day1 samples
## Setup
### Bioconductor and CRAN libraries used
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library(pheatmap)
library(DEGreport)
library(tximport)
library(ggplot2)
library(ggrepel)
library(knitr)
library(AnnotationHub)## Loading required package: BiocFileCache## Loading required package: dbplyr##
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## filterggplot2::theme_set(theme_light(base_size = 14))
opts_chunk[["set"]](
cache = FALSE,
dev = c("png", "pdf"),
error = TRUE,
highlight = TRUE,
message = FALSE,
prompt = FALSE,
tidy = FALSE,
warning = FALSE)# Have a folder called `data`, and copy your Salmon folders here from the cluster.
## List all directories containing data
### change the pattern to something specific to your Salmon folders
samples <- list.files(path = "./data/final",
full.names = T,
pattern = "^S")
## Obtain a vector of all filenames including the path
files <- file.path(samples, "salmon", "quant.sf")
files## [1] "./data/final/S3154-2/salmon/quant.sf"
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## [44] "./data/final/S4295-5/salmon/quant.sf"## Since all quant files have the same name it is useful to have names for each element
### change the string in str_replace so the pattern matches your filenames
names(files) <- str_replace(samples, "./data/final/", "") # Load the data and metadata
meta <- read_csv("tables/metadata_corrected.csv") %>%
column_to_rownames(var = "samplename") %>%
dplyr::filter(treatment == "pre") %>%
drop_na(response)
protein_coding_genes <- read_csv("tables/ensembl_w_description.protein_coding.csv")# Connect to AnnotationHub
ah <- AnnotationHub()
# Query AnnotationHub
hs_ens <- query(ah, c("Homo sapiens", "EnsDb"))
# Get Ensembl94 - used in bcbio
hs_ens <- hs_ens[["AH64923"]]
# Extract gene-level information
txdb <- transcripts(hs_ens,
return.type = "data.frame") %>%
dplyr::select(tx_id, gene_id)
genedb <- genes(hs_ens,
return.type = "data.frame") %>%
dplyr::select(gene_id, gene_name, symbol)
gene_symbol <- genedb %>% dplyr::select(gene_id, symbol)
hsdb <- inner_join(txdb, genedb)
write.table(hsdb,
file = "data/ensembl94_hg38_annotations.txt",
sep = "\t",
row.names = F,
quote = F)# Read in a tx2gene file with transcript identifiers in the first column and gene identifiers in the second column
#wormdb <- read.table("ensembl94_WBcel235_annotations.txt", sep="\t", header=T)
tx2gene <- hsdb[, c("tx_id", "gene_id")]
# Run tximport
files <- files[rownames(meta)]
txi_file <- "data/txi.day1.RDS"
if (file.exists(txi_file)){
txi <- readRDS(txi_file)
}else{
txi <- tximport(files,
type = "salmon",
tx2gene = tx2gene,
countsFromAbundance = "lengthScaledTPM",
ignoreTxVersion = FALSE)
saveRDS(txi, txi_file)
}
# Look at the counts
class(txi)## [1] "list"attributes(txi)## $names
## [1] "abundance" "counts" "length"
## [4] "countsFromAbundance"txi$counts %>% View()2 Checking to see that the transcript to gene mapping is correct
When you have annotations that are from a different source from your reference you can run into problems (i.e lose genes). Some checks you can do before proceeding:
- Look at the dimensions of your count matrix. Do you have ~20k genes present?
dim(txi$counts) - When running
tximport()you will get a message in your console. If you see something liketranscripts missing from tx2genestart troubleshooting.
dim(txi$counts)## [1] 58735 203 Sanity check that metadata matches your expression
It is always a good idea to check if:
- Do you have expression data for all samples listed in your metadata?
- Are the samples in your expression data in the same order as your metadata?
### Check that sample names match in both files
all(colnames(txi$counts) %in% rownames(meta))## [1] TRUE# Not the same? Make them the same
### This will change depending on what names you have listed!
#paste0(meta$samplename, "_", meta$library)
#rownames(meta) <- paste0(meta$samplename, "_", meta$library)
#meta$genotype <- relevel(meta$genotype, ref="Wildtype")
### Check that sample names match in both files
all(colnames(txi$counts) %in% rownames(meta))## [1] TRUE### Check that all samples are in the same order
meta <- meta[colnames(txi$counts),]
all(colnames(txi$counts) == rownames(meta))## [1] TRUE4 Run DESeq2
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing5 Wald test
Here we subset protein coding genes.
## Create DESeq2Dataset object
dds_file <- "data/dds.day1.RDS"
meta$treatment <- as.factor(meta$treatment)
meta$response <- as.factor(meta$response)
meta$er <- as.factor(meta$er)
meta$date_of <- as.factor(meta$date_of)
meta$tumor_percentage <- as.factor(meta$tumor_percentage)
meta$tumor_percentage_high <- as.factor(meta$tumor_percentage_high)
non_responders <- meta %>% dplyr::filter(study_id %in% c(2, 19)) %>% row.names()
if (file.exists(dds_file)){
dds <- readRDS(dds_file)
}else{
dds <- DESeqDataSetFromTximport(txi,
colData = meta,
design = ~response)
#dds <- dds[,!colnames(dds) %in% non_responders]
design(dds) <- formula(~response + er + tumor_percentage_high + date_of)
# subset protein-coding genes
pc_genes <- intersect(protein_coding_genes$ensembl_gene_id, row.names(dds))
dds <- dds[pc_genes,]
# 100 reads / 20 samples
keep <- rowSums(counts(dds)) >= 100
dds <- dds[keep,]
# Run DESeq2
dds <- DESeq(dds)
saveRDS(dds, dds_file)
}6 DEGreport QC
6.1 Size factor QC - samples 1-20
counts <- counts(dds, normalized = TRUE)
design <- as.data.frame(colData(dds))
degCheckFactors(counts[, 1:20])
7 Mean-Variance QC plots
7.1 response
res <- results(dds)
degQC(counts, design[["response"]], pvalue = res[["pvalue"]])
7.2 ER
degQC(counts, design[["er"]], pvalue = res[["pvalue"]])
7.3 tumor_percentage_high
degQC(counts, design[["tumor_percentage_high"]], pvalue = res[["pvalue"]])
8 Covariates effect on count data
mdata <- colData(dds) %>% as.data.frame() %>%
dplyr::select(response, er, date_of, tumor_percentage_high)
#resCov <- degCovariates(log2(counts(dds)+0.5), mdata)mdata %>% ggplot(aes(tumor_percentage_high, fill = response)) + geom_bar(position = "dodge2")
9 Covariates correlation with metrics
cor <- degCorCov(mdata)
mdata %>% ggplot(aes(date_of, fill = response)) + geom_bar(position = "dodge2")
10 Sample-level QC analysis
### Transform counts for data visualization (unsupervised analysis)
rld_file <- "data/rld.day1.RDS"
if (file.exists(rld_file)){
rld <- readRDS(rld_file)
}else{
rld <- rlog(dds, blind = TRUE)
saveRDS(rld, rld_file)
}
class(rld) # what type of object is this## [1] "DESeqTransform"
## attr(,"package")
## [1] "DESeq2"# we also need just a matrix of transformed counts
rld_mat <- assay(rld)10.1 PCA - response
# Use the DESeq2 function
plotPCA(rld, intgroup = c("response"))
10.2 PCA - ER
# Use the DESeq2 function
plotPCA(rld, intgroup = c("er"))
10.3 PCA - tumor_percentage
# Use the DESeq2 function
plotPCA(rld, intgroup = c("tumor_percentage"))
10.4 PCA - tumor_percentage_high
# Use the DESeq2 function
plotPCA(rld, intgroup = c("tumor_percentage_high"))
10.5 PCA - date_of
# Use the DESeq2 function
plotPCA(rld, intgroup = c("date_of"))
11 Inter-correlation analysis
11.1 Without study_id
# Correlation matrix
rld_cor <- cor(rld_mat)
meta$study_id <- as.factor(meta$study_id)
# Create annotation file for samples
annotation <- meta[, c("response", "er", "tumor_percentage_high", "date_of")]
# Change colors
heat.colors <- brewer.pal(6, "Blues")
# Plot heatmap
pheatmap(rld_cor,
annotation = annotation,
border = NA,
fontsize = 20)
11.2 With study_id
# Correlation matrix
rld_cor <- cor(rld_mat)
meta$study_id <- as.factor(meta$study_id)
# Create annotation file for samples
annotation <- meta[, c("response", "er", "tumor_percentage_high", "date_of", "study_id")]
# Change colors
heat.colors <- brewer.pal(6, "Blues")
# Plot heatmap
pheatmap(rld_cor,
annotation = annotation,
border = NA,
fontsize = 20)
12 Response Yes vs No for Day 1- see Table9
# Get results for rescue vs wt
contrast <- c("response", "Yes", "No")
resResponse <- results(dds, contrast = contrast, alpha = 0.05)
length(which(resResponse$padj < 0.05))## [1] 15# Add annotations
resResponse_tb <- resResponse %>%
data.frame() %>%
rownames_to_column(var = "gene") %>%
as_tibble() %>%
left_join(gene_symbol, by = c("gene" = "gene_id"))
resResponse_tb_significant <- dplyr::filter(resResponse_tb, padj < 0.05)
samples_no <- meta %>% dplyr::filter(response == "No") %>% row.names()
counts_no <- txi$abundance %>%
as.data.frame() %>%
dplyr::select(any_of(samples_no)) %>%
rowSums() %>%
as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id")
colnames(counts_no) <- c("ensembl_gene_id", "no_expression_mean_tpm")
samples_yes <- meta %>% dplyr::filter(response == "Yes") %>% row.names()
counts_yes <- txi$abundance %>%
as.data.frame() %>%
dplyr::select(any_of(samples_yes)) %>%
rowSums() %>%
as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id")
colnames(counts_yes) <- c("ensembl_gene_id", "yes_expression_mean_tpm")
counts_yes <-counts_yes %>%
left_join(counts_no,
by = c("ensembl_gene_id" = "ensembl_gene_id"))
resResponse_tb_significant <- resResponse_tb_significant %>%
left_join(counts_yes, by = c("gene" = "ensembl_gene_id"))
write_csv(resResponse_tb_significant,
"tables/T9.DE_response_day1.csv")
# Separate into up and down-regulated gene sets
sigResponse_up <- rownames(resResponse)[which(resResponse$padj < 0.01 & resResponse$log2FoldChange > 0)]
sigResponse_down <- rownames(resResponse)[which(resResponse$padj < 0.01 & resResponse$log2FoldChange < 0)]13 ER : Positive vs Negative for Day1 - Table 10
contrast <- c("er", "Positive", "Negative")
resER <- results(dds, contrast = contrast, alpha = 0.05)
length(which(resER$padj < 0.05))## [1] 571# Add annotations
resER_tb <- resER %>%
data.frame() %>%
rownames_to_column(var = "gene") %>%
as_tibble() %>%
left_join(gene_symbol, by = c("gene" = "gene_id"))
resER_tb_significant <- dplyr::filter(resER_tb, padj < 0.05)
samples_pos <- meta %>% dplyr::filter(er == "Positive") %>% row.names()
counts_pos <- txi$abundance %>%
as.data.frame() %>%
dplyr::select(any_of(samples_pos)) %>%
rowMeans() %>%
as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id")
colnames(counts_pos) <- c("ensembl_gene_id", "Positive_expression_mean_tpm")
samples_neg <- meta %>% dplyr::filter(er == "Negative") %>% row.names()
counts_neg <- txi$abundance %>%
as.data.frame() %>%
dplyr::select(any_of(samples_neg)) %>%
rowMeans() %>%
as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id")
colnames(counts_neg) <- c("ensembl_gene_id", "Negative_expression_mean_tpm")
counts_pos <-counts_pos %>%
left_join(counts_neg,
by = c("ensembl_gene_id" = "ensembl_gene_id"))
resER_tb_significant <- resER_tb_significant %>%
left_join(counts_pos, by = c("gene" = "ensembl_gene_id"))
write_csv(resER_tb_significant,
"tables/T10.DE_ER.day1.csv")
# Separate into up and down-regulated gene sets
sigER_up <- rownames(resER)[which(resER$padj < 0.01 & resER$log2FoldChange > 0)]
sigER_down <- rownames(resER)[which(resER$padj < 0.01 & resER$log2FoldChange < 0)]14 tumor_percentage_high : High vs Low for Day1- Table 11
contrast <- c("tumor_percentage_high", "high", "low")
resTP <- results(dds, contrast = contrast, alpha = 0.05)
length(which(resTP$padj < 0.05))## [1] 7# Add annotations
resTP_tb <- resTP %>%
data.frame() %>%
rownames_to_column(var = "gene") %>%
as_tibble() %>%
left_join(gene_symbol, by = c("gene" = "gene_id"))
resTP_tb_significant <- dplyr::filter(resTP_tb, padj < 0.05)
samples_high <- meta %>% dplyr::filter(tumor_percentage_high == "high") %>% row.names()
counts_high <- txi$abundance %>%
as.data.frame() %>%
dplyr::select(any_of(samples_high)) %>%
rowMeans() %>%
as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id")
colnames(counts_high) <- c("ensembl_gene_id", "High_expression_mean_tpm")
samples_low <- meta %>% dplyr::filter(tumor_percentage_high == "low") %>% row.names()
counts_low <- txi$abundance %>%
as.data.frame() %>%
dplyr::select(any_of(samples_low)) %>%
rowMeans() %>%
as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id")
colnames(counts_low) <- c("ensembl_gene_id", "Low_expression_mean_tpm")
counts_high <-counts_high %>%
left_join(counts_low,
by = c("ensembl_gene_id" = "ensembl_gene_id"))
resTP_tb_significant <- resTP_tb_significant %>%
left_join(counts_high, by = c("gene" = "ensembl_gene_id"))
write_csv(resTP_tb_significant,
"tables/T11.DE_tumor_percentage_high.day1.csv")
# Separate into up and down-regulated gene sets
sigTP_up <- rownames(resTP)[which(resTP$padj < 0.01 & resTP$log2FoldChange > 0)]
sigTP_down <- rownames(resTP)[which(resTP$padj < 0.01 & resTP$log2FoldChange < 0)]15 date_of: 20180323 vs 20180228 - for Day1: Table 12
contrast <- c("date_of", "20180323", "20180228")
resDO <- results(dds, contrast = contrast, alpha = 0.05)
length(which(resDO$padj < 0.05))## [1] 30# Add annotations
resDO_tb <- resDO %>%
data.frame() %>%
rownames_to_column(var = "gene") %>%
as_tibble() %>%
left_join(gene_symbol, by = c("gene" = "gene_id"))
resDO_tb_significant <- dplyr::filter(resDO_tb, padj < 0.05)
samples_23 <- meta %>% dplyr::filter(date_of == "20180323") %>% row.names()
counts_23 <- txi$abundance %>%
as.data.frame() %>%
dplyr::select(any_of(samples_23)) %>%
rowMeans() %>%
as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id")
colnames(counts_23) <- c("ensembl_gene_id", "20180323_expression_mean_tpm")
samples_28 <- meta %>% dplyr::filter(date_of == "20180228") %>% row.names()
counts_28 <- txi$abundance %>%
as.data.frame() %>%
dplyr::select(any_of(samples_28)) %>%
rowMeans() %>%
as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id")
colnames(counts_28) <- c("ensembl_gene_id", "20180228_expression_mean_tpm")
counts_23 <-counts_23 %>%
left_join(counts_28,
by = c("ensembl_gene_id" = "ensembl_gene_id"))
resDO_tb_significant <- resDO_tb_significant %>%
left_join(counts_23, by = c("gene" = "ensembl_gene_id"))
write_csv(resDO_tb_significant,
"tables/T12.DE_date_of.day1.csv")
# Separate into up and down-regulated gene sets
sigDO_up <- rownames(resDO)[which(resDO$padj < 0.01 & resDO$log2FoldChange > 0)]
sigDO_down <- rownames(resDO)[which(resDO$padj < 0.01 & resDO$log2FoldChange < 0)]16 Visualization
Gene example
d <- plotCounts(dds,
gene = "ENSG00000130234",
intgroup = "response",
returnData = TRUE)
ggplot(d, aes(x = response, y = count)) +
geom_point(position = position_jitter(w = 0.1, h = 0)) +
geom_text_repel(aes(label = rownames(d))) +
theme_bw(base_size = 10) +
ggtitle("ACE2") +
theme(plot.title = element_text(hjust = 0.5)) +
scale_y_log10()
# Add a column for significant genes
resResponse_tb <- resResponse_tb %>% mutate(threshold = padj < 0.01)
ggplot(resResponse_tb) +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
ggtitle("Response Yes vs No") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-10,10)) +
scale_y_continuous(limits = c(0, 7.5))+
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
# Add a column for significant genes
resER_tb <- resER_tb %>% mutate(threshold = padj < 0.01)
ggplot(resER_tb) +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
ggtitle("ER: Positive vs Negative") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-10,10)) +
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
# Add a column for significant genes
resTP_tb <- resTP_tb %>% mutate(threshold = padj < 0.01)
ggplot(resTP_tb) +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
ggtitle("Tumor_percentage_high: High vs Low") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-10,10)) +
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
# Add a column for significant genes
resDO_tb <- resDO_tb %>% mutate(threshold = padj < 0.01)
ggplot(resDO_tb) +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
ggtitle("Dafe of: 20180323 vs 20180228") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-10,10)) +
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
# Heatmaps
# Create a matrix of normalized expression
sig_up <- resResponse_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resResponse_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)
row_annotation <- gene_symbol %>%
as_tibble() %>%
dplyr::filter(gene_id %in% sig)
plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id") %>%
left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>%
drop_na(symbol)
plotmat$ensembl_gene_id <- NULL
plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()
# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")
# Plot heatmap
pheatmap(plotmat,
scale = "row",
show_rownames = TRUE,
border = FALSE,
annotation = meta[, c("response"), drop = FALSE],
main = "Top 50 Up- and Down- regulated genes in Response: Yes vs No",
fontsize = 20)
# Create a matrix of normalized expression
sig_up <- resER_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resER_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)
row_annotation <- gene_symbol %>%
as_tibble() %>%
dplyr::filter(gene_id %in% sig)
plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id") %>%
left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>%
drop_na(symbol)
plotmat$ensembl_gene_id <- NULL
plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()
# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")
# Plot heatmap
pheatmap(plotmat,
scale = "row",
show_rownames = TRUE,
border = FALSE,
annotation = meta[, c("er"), drop = FALSE],
main = "Top 50 Up- and Down- regulated genes in ER: positive vs negative",
fontsize = 20)
# Create a matrix of normalized expression
sig_up <- resTP_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resTP_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)
row_annotation <- gene_symbol %>%
as_tibble() %>%
dplyr::filter(gene_id %in% sig)
plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id") %>%
left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>%
drop_na(symbol)
plotmat$ensembl_gene_id <- NULL
plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()
# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")
# Plot heatmap
pheatmap(plotmat,
scale = "row",
show_rownames = TRUE,
border = FALSE,
annotation = meta[, c("tumor_percentage_high"), drop = FALSE],
main = "Top Up/Down-regulated genes in Tumor_percentage_high: high vs low",
fontsize = 20)
# Create a matrix of normalized expression
sig_up <- resDO_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resDO_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)
row_annotation <- gene_symbol %>%
as_tibble() %>%
dplyr::filter(gene_id %in% sig)
plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id") %>%
left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>%
drop_na(symbol)
plotmat$ensembl_gene_id <- NULL
plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()
# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")
# Plot heatmap
pheatmap(plotmat,
scale = "row",
show_rownames = TRUE,
border = FALSE,
annotation = meta[, c("response"), drop = FALSE],
main = "Top 50 Up- and Down- regulated genes in date_of: 20180323 vs 20180228",
fontsize = 20)
17 R session
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Fedora 32 (Workstation Edition)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.12.so
##
## locale:
## [1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
## [5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
## [7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ensembldb_2.14.0 AnnotationFilter_1.14.0
## [3] GenomicFeatures_1.42.1 AnnotationDbi_1.52.0
## [5] AnnotationHub_2.22.0 BiocFileCache_1.14.0
## [7] dbplyr_2.1.0 knitr_1.30
## [9] ggrepel_0.9.1 tximport_1.18.0
## [11] DEGreport_1.26.0 pheatmap_1.0.12
## [13] RColorBrewer_1.1-2 forcats_0.5.1
## [15] stringr_1.4.0 dplyr_1.0.5
## [17] purrr_0.3.4 readr_1.4.0
## [19] tidyr_1.1.3 tibble_3.1.0
## [21] ggplot2_3.3.3 tidyverse_1.3.0
## [23] DESeq2_1.30.1 SummarizedExperiment_1.20.0
## [25] Biobase_2.50.0 MatrixGenerics_1.2.1
## [27] matrixStats_0.58.0 GenomicRanges_1.42.0
## [29] GenomeInfoDb_1.26.2 IRanges_2.24.1
## [31] S4Vectors_0.28.1 BiocGenerics_0.36.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.2.1
## [3] circlize_0.4.12 plyr_1.8.6
## [5] lazyeval_0.2.2 ConsensusClusterPlus_1.54.0
## [7] splines_4.0.3 BiocParallel_1.24.1
## [9] digest_0.6.27 htmltools_0.5.1.1
## [11] fansi_0.4.2 magrittr_2.0.1
## [13] memoise_2.0.0 cluster_2.1.0
## [15] limma_3.46.0 ComplexHeatmap_2.6.2
## [17] Biostrings_2.58.0 annotate_1.68.0
## [19] Nozzle.R1_1.1-1 modelr_0.1.8
## [21] askpass_1.1 prettyunits_1.1.1
## [23] colorspace_2.0-0 blob_1.2.1
## [25] rvest_1.0.0 rappdirs_0.3.3
## [27] haven_2.3.1 xfun_0.19
## [29] crayon_1.4.1 RCurl_1.98-1.2
## [31] jsonlite_1.7.1 genefilter_1.72.1
## [33] survival_3.2-7 glue_1.4.2
## [35] gtable_0.3.0 zlibbioc_1.36.0
## [37] XVector_0.30.0 GetoptLong_1.0.5
## [39] DelayedArray_0.16.2 shape_1.4.5
## [41] scales_1.1.1 DBI_1.1.1
## [43] edgeR_3.32.1 Rcpp_1.0.6
## [45] progress_1.2.2 xtable_1.8-4
## [47] lasso2_1.2-21.1 tmvnsim_1.0-2
## [49] clue_0.3-58 bit_4.0.4
## [51] httr_1.4.2 ellipsis_0.3.1
## [53] farver_2.1.0 pkgconfig_2.0.3
## [55] reshape_0.8.8 XML_3.99-0.5
## [57] locfit_1.5-9.4 utf8_1.1.4
## [59] labeling_0.4.2 tidyselect_1.1.0
## [61] rlang_0.4.10 later_1.1.0.1
## [63] munsell_0.5.0 BiocVersion_3.12.0
## [65] cellranger_1.1.0 tools_4.0.3
## [67] cachem_1.0.4 cli_2.3.1
## [69] generics_0.1.0 RSQLite_2.2.3
## [71] broom_0.7.5 evaluate_0.14
## [73] fastmap_1.1.0 ggdendro_0.1.22
## [75] yaml_2.2.1 bit64_4.0.5
## [77] fs_1.5.0 nlme_3.1-149
## [79] mime_0.9 xml2_1.3.2
## [81] biomaRt_2.46.3 compiler_4.0.3
## [83] rstudioapi_0.13 curl_4.3
## [85] png_0.1-7 interactiveDisplayBase_1.28.0
## [87] reprex_1.0.0 geneplotter_1.68.0
## [89] stringi_1.5.3 lattice_0.20-41
## [91] ProtGenerics_1.22.0 Matrix_1.2-18
## [93] psych_2.0.12 vctrs_0.3.6
## [95] pillar_1.5.1 lifecycle_1.0.0
## [97] BiocManager_1.30.10 GlobalOptions_0.1.2
## [99] cowplot_1.1.1 bitops_1.0-6
## [101] rtracklayer_1.50.0 httpuv_1.5.5
## [103] R6_2.5.0 promises_1.2.0.1
## [105] MASS_7.3-53 assertthat_0.2.1
## [107] openssl_1.4.3 rjson_0.2.20
## [109] withr_2.4.1 GenomicAlignments_1.26.0
## [111] Rsamtools_2.6.0 mnormt_2.0.2
## [113] GenomeInfoDbData_1.2.4 hms_1.0.0
## [115] grid_4.0.3 rmarkdown_2.5
## [117] Cairo_1.5-12.2 logging_0.10-108
## [119] shiny_1.6.0 lubridate_1.7.10